A hyperspectral image can predict tropical tree growth rates in single-species stands

Remote sensing is increasingly needed to meet the critical demand for estimates of forest structure and composition at landscape to continental scales. Hyperspectral images can detect tree canopy properties, including species identity, leaf chemistry and disease. Tree growth rates are related to the...

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Bibliographic Details
Published in:Ecological applications 2016-12, Vol.26 (8), p.2369-2375
Main Authors: Caughlin, T. Trevor, Graves, Sarah J., Asner, Gregory P., van Breugel, Michiel, Hall, Jefferson S., Martin, Roberta E., Ashton, Mark S., Bohlman, Stephanie A.
Format: Article
Language:English
Subjects:
canopy biology
Communications
Demography
field planting trials
forest dynamics
Forests
hyperspectral
light detection and ranging
Panama
Plant Leaves
plantation
precision forestry
reforestation
remote sensing
tree demography
Trees
Tropical Climate
tropical forest
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Summary:Remote sensing is increasingly needed to meet the critical demand for estimates of forest structure and composition at landscape to continental scales. Hyperspectral images can detect tree canopy properties, including species identity, leaf chemistry and disease. Tree growth rates are related to these measurable canopy properties but whether growth can be directly predicted from hyperspectral data remains unknown. We used a single hyperspectral image and light detection and ranging-derived elevation to predict growth rates for 20 tropical tree species planted in experimental plots. We asked whether a consistent relationship between spectral data and growth rates exists across all species and which spectral regions, associated with different canopy chemical and structural properties, are important for predicting growth rates. We found that a linear combination of narrowband indices and elevation is correlated with standardized growth rates across all 20 tree species (R² = 53.70%). Although wavelengths from the entire visible-to-shortwave infrared spectrum were involved in our analysis, results point to relatively greater importance of visible and near-infrared regions for relating canopy reflectance to tree growth data. Overall, we demonstrate the potential for hyperspectral data to quantify tree demography over a much larger area than possible with field-based methods in forest inventory plots.
ISSN:1051-0761
1939-5582
DOI:10.1002/eap.1436